| 1Sogeti PowerPoint Referentie 2014

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| 5Sogeti PowerPoint Referentie 2014

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| 7Sogeti PowerPoint Referentie 2014Classic

| 8Sogeti PowerPoint Referentie 2014Monitoring

| 9Sogeti PowerPoint Referentie 2014Weather forecasting

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|Digital Twin

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us.sogeti.com15

USE DATA TO SOLVE QA CHALLENGES!

us.sogeti.com

We generate and manage data about development, QA activities, Customer Assurance,…

Artificial Intelligence

Prediction

Data Analysis

TestingCustomer AssuranceDevelopment

Data Management

We transform data to information for analysis through BI capabilities and customized dashboards.

We use predictive models in order to predict defects, patterns, critical

areas, future efforts,… from historical and present data.

We automate AI-based actions based on experience and predictions, in order to

enhance quality management, resources optimization and time-to-market.

Non-structureddata

WHAT CAN WE DO WITH DATA?

us.sogeti.com

ENABLE DATA-DRIVEN DECISIONS

TestCase1

TestCase2

TestCase3

TestCase8

TestCase9

TestCase1

TestCase8

TestCase4

TestCase1

TestCase2

TestCase6

TestCase8

TestCase9

Test set A

Test set B

Test set C

Which set of test cases will be most productive?

TestCase3

The objective: Maximizeproductivity of test cases (*)

(*) Test sets are missing up to 30% of relevant tests and/or containingirrelevant test scenarios. This contextrequires prioritization and optimization.

TestCase5

TestCase7

us.sogeti.com

ENABLE DATA-DRIVEN DECISIONS

TestCase1

TestCase2

TestCase3

TestCase8

TestCase9

TestCase1

TestCase8

TestCase4

TestCase1

TestCase2

TestCase6

TestCase8

TestCase9

Test set A

Test set B

Test set C

Which set of test cases will be most productive?

TestCase3

TestCase5

TestCase7

Functional area

LOGIN

Functional area

TICKET PURCHASE

Functional area

LOGIN WITH FACE ID

• There are no recent commits

• Important function

• Low defects rates trend

• 25 recent commits

• Core business function

• Test Case 4 with high rate of associated defects

• Regular trend of defects in this area

• Currently, there are defects pending to be fixed

• Negative comments in the appstore

• 2 recent commits

• Additional functionality

• Stabilized defects trend

• No pending defects to be fixed

us.sogeti.com

OPTIMIZE QA PROCESSES - EXAMPLE

Avatar

Watson API

Conversation Module

(Watson Conversation Service)

Trigger actions

The bot is an AI interface that consists of an Avatar (with Text-to-Speech and Speech-to-Text facilities) that is able to follow a conversation, compute information from the Cognitive QA platform and trigger actions.

CognitiveQAreasoning

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New quality attributes make the difference

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Distinguish testing OF and testing WITH intelligent machines

|Existing New

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Mechanical, Electrical, Information processing

No. 2001 is seen on the rollers at the Vitry test plant Gresley Society Collection, 1934

Testing an electric stove, 1970’s © handyman

The 7 generic testing activities of TMap® 1995/2006

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Testing intelligent machines is different…

Traditional testing comparesexpected and actual outcome.

For intelligent machines predictingoutcome is difficult; todayscorrect result may differ fromtomorrows correct result, so:- Define tolerances- Control input- Test the machine in its

intended environment- Use new quality characteristics

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ISO25010 – Product Quality characteristics

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ISO25010 + new quality characteristics

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Drie nieuwe attributen en één subattribuut

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Sog

eti

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Super intelligence & super stupidity

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Machine learning – ability to learn

<<< vervangen door filmclip >>>

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Ability to learn and to unlearn

Machine learning• Continued learning• Frozen

Examination• One-time• Continuous

Un-learning

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How to unlearn AI’s ?

Rule-based AI:• Change the programmed rules

Reinformement learning AI:• Change goal(s)• Control input• Feed new learning data

U N

|Morality: Ethics and Privacy

Scene uit de film “Robot and Frank” 2012

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Morality: Ethics and Privacy

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Embodiment in Cobotics

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| 38Sogeti PowerPoint Referentie 2014